Automated analysis of electronic medical record data reflects the pathophysiology of operative complications

Abstract

We hypothesized that a novel algorithm that uses data from the electronic medical record (EMR) from multiple clinical and biometric sources could provide early warning of organ dysfunction in patients with high risk for postoperative complications and sepsis. Operative patients undergoing colorectal procedures were evaluated. The Rothman Index (RI) is a predictive model based on heuristic equations derived from 26 variables related to inpatient care. The RI integrates clinical nursing observations, bedside biometrics, and laboratory data into a continuously updated, numeric physiologic assessment, ranging from 100 (unimpaired) to -91. The RI can be displayed within the EMR as a graphic trend, with a decreasing trend reflecting physiologic dysfunction. Patients undergoing colorectal procedures between June and October 2011 were evaluated to determine correlation of initial RI, average inpatient RI, and lowest RI to incidence of complications and/or postoperative sepsis. Patients were stratified by color-coded RI risk group (100-65, blue; 64-40, yellow; <40 red). One-way or repeated-measures analysis of variance was used to compare groups by age, number of complications, and presence of sepsis defined by discharge International Classification of Diseases, 9(th) Revision, codes. Mean direct cost of care and duration of stay also was calculated for each group. The overall incidence of perioperative complications in the 124 patient cohort was 51% (n = 64 patients). The 261 complications sustained by this group represented 82 distinct diagnoses. The 10 patients with sepsis (8%) experienced a 40% mortality. Analysis of initial RI for the population stratified by number of complications and/or sepsis demonstrated a risk-related difference. With progressive onset of complications, the RI decreased, suggesting worsening physiologic dysfunction and linear increase in direct cost of care. These findings demonstrate that EMR data can be automatically compiled into an objective metric that reflects patient risk and changing physiologic state. The automated process of continuous update reflects a physiologic trajectory associated with evolving organ system dysfunction indicative of postoperative complications. Early intervention based on these trends may guide preoperative counseling, enhance pre-emptive management of adverse occurrences, and improve cost-efficiency of care.